AIMC Topic: Image Processing, Computer-Assisted

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Automatic segmentation and labelling of wrist bones in four-dimensional computed tomography datasets via deep learning.

The Journal of hand surgery, European volume
This study developed a deep learning model for fully automatic segmentation and labelling of wrist bones from four-dimensional computed tomography (4DCT) scans. This is a crucial step towards implementing 4DCT for diagnosing wrist ligament lesions, r...

Multi-scale feature selection network for lightweight image super-resolution.

Neural networks : the official journal of the International Neural Network Society
Recently, many super-resolution (SR) methods based on convolutional neural networks (CNNs) have achieved superior performance by utilizing deep and heavy models, which may not be suitable for real-world low-budget devices. To address this issue, we p...

Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: "Batch effect" in MR images, due to vendor-specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability.

HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding.

Medical & biological engineering & computing
Semi-supervised learning methods have been attracting much attention in medical image segmentation due to the lack of high-quality annotation. To cope with the noise problem of pseudo-label in semi-supervised medical image segmentation and the limita...

Deep learning methods for blood flow reconstruction in a vessel with contrast enhanced x-ray computed tomography.

International journal for numerical methods in biomedical engineering
The reconstruction of blood velocity in a vessel from contrast enhanced x-ray computed tomography projections is a complex inverse problem. It can be formulated as reconstruction problem with a partial differential equation constraint. A solution can...

Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability.

Molecular imaging and biology
PURPOSE: Positron emission tomography (PET) image quality can be improved by higher injected activity and/or longer acquisition time, but both may often not be practical in preclinical imaging. Common preclinical radioactive doses (10 MBq) have been ...

Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization.

Scientific reports
In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such studies, wh...

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Computer methods and programs in biomedicine
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabili...

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Computers in biology and medicine
Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the appl...

Deep-learning-based cross-modality translation from Stokes image to bright-field contrast.

Journal of biomedical optics
SIGNIFICANCE: Mueller matrix (MM) microscopy has proven to be a powerful tool for probing microstructural characteristics of biological samples down to subwavelength scale. However, in clinical practice, doctors usually rely on bright-field microscop...